Multiscale Lung Tissue Simulator for Sars-Cov-2 Infection and Damage

The 2019 novel coronavirus, SARS-CoV-2, is an emerging pathogen of critical significance to international public health. Knowledge of the interplay between molecular-scale virus-receptor interactions, single-cell viral replication, intracellular-scale viral transport, and emergent tissue-scale viral propagation is limited. Moreover, little is known about immune system-virus-tissue interactions and how these can result in low-level (asymptomatic) infections in some cases and acute respiratory distress syndrome (ARDS) and other tissue damage in others, particularly with respect to presentation in different age groups or pre-existing inflammatory risk factors like diabetes. A critical question for treatment and protection is why it appears that the severity of infection may correlate with the initial level of virus exposure. Given the nonlinear interactions within and among each of these processes, multiscale simulation models can shed light on the emergent dynamics that lead to divergent outcomes, identify actionable “choke points” for pharmacologic interactions, screen potential therapies, and identify potential biomarkers that differentiate patient outcomes. Given the complexity of the problem and the acute need for an actionable model to guide therapy discovery and optimization, we have introduced a prototype of a multiscale mathematical model of SARS-CoV-2 dynamics in lung and intestinal tissue that will be iteratively refined. The first prototype model was built and shared as open source, cloud-hosted and web-executed code and simulator¹ in under 12 hours in March 2020. We have assembled an international, multi-disciplinary collaborative team called the SARS-CoV-2 Tissue Simulation Coalition with academic, industry, and non-profit sector participants. Additional domain experts have continued to join the Coalition throughout its first month. We have documented the rapid model development process with a preprint submission², and second version is to be released within a few days. In a sustained community effort, this model is integrating data and expertise across virology, immunology, mathematical biology, quantitative systems physiology and pharmacology, cloud and high performance computing, and other domains to accelerate our response to this critical health threat.

We are developing an open-source, multi-scale tissue simulator that can be used to investigate mechanisms of intracellular viral replication, infection of epithelial cells, host immune response, and tissue damage. The aim of this project is to concentrate community modeling efforts to create a comprehensive multiscale simulation framework that can subsequently be calibrated, validated, and used to rapidly explore and optimize therapeutic interventions for COVID-19. Once the prototype has been completed (after several design iterations), this coalition will transition to maintain and support the simulation and data collection/curation framework and aggregate calibrated/validated parameter values. To address the acute need for rapid access to an actionable model, we are using a community-driven coalition and best open science practices to build and iteratively refine the model.

Multiple chemical engineers are in the SARS-CoV-2 Tissue Simulation Coalition. Even from the first prototype version, the model applies chemical engineering fundamentals to systemically analyze dynamic interactions between processes involved in SARS-CoV-2 infection and damage. Chemical reactions, enzyme kinetics, and molecular binding interactions are simulated for viral and host cellular processes. Transport phenomena including extracellular diffusion of secreted chemicals and cell endocytosis and exocytosis of viral materials are explicitly included in the current version. In future versions, we plan to consider convection of aerosol droplets laden with virus throughout the lungs, penetration of the virus through the mucus lining the airways, signaling to systemic host responses, and distribution of systemic immune responses into local tissues. Core concepts in mass and population balances, numerical methods, and mathematical modeling with ordinary and partial differential equations are also used throughout the project. Stochastic and multiscale modeling approaches are also incorporated to address challenges at different length and times scales and to allow for uncertainty and biological variations. This is not simply a theoretical and computational study. Experimental and clinical collaborators who are setting up for animal and cell culture experiments in April 2020 are also part of the Coalition. It is anticipated that these data as they become available will be incorporated into multiple versions of the model before the AIChE Annual Meeting.

1. Heiland, R.; Macklin, P. PhysiCell model for COVID19. https://nanohub.org/tools/pc4covid19
2. Wang, Y. N.; Heiland, R.; Craig, M.; Davis, C.; Ford Versypt, A. N.; Jenner, A.; Ozik, J.; Collier, N.; Cockrell, C.; Becker, A.; An, G.; Glazier, J. A.; Narayanan, A.; Smith, A. M.; Macklin, P. bioRxiv 2020, 2020.04.02.019075, https://doi.org/10.1101/2020.04.02.019075.